Emergency Situation Awareness During Natural Disasters Using Density-Based Adaptive Spatiotemporal Clustering

Abstract

AbstractWith the increase in the popularity of social media as well as the emergence of easy-to-use geo-mobile applications on smartphones, a huge amount of geo-annotated data is posted on social media sites. To enhance emergency situation awareness, these geo-annotated data are expected to be used in a new medium. In particular, geotagged tweets on Twitter are used by local governments to determine the situation accurately during natural disasters. Geotagged tweets are referred to as georeferenced documents; they include not only a short text message but also the posting time and location. In this paper, we propose a new spatiotemporal analysis method for emergency situation awareness during natural disasters using \((\epsilon ,\tau )\)-density-based adaptive spatiotemporal clustering. Such clustering can identify bursty local areas by using adaptive spatiotemporal clustering criteria considering local spatiotemporal densities. Extracting \((\epsilon ,\tau )\)-density-based adaptive spatiotemporal clusters allows the proposed method to analyze emergency situations such as natural disasters in real time. The experimental results showed that the proposed method can analyze emergency situations related to the weather in Japan more sensitively compared with our previous method.KeywordsEmergency situation awarenessDensity-based spatiotemporal clusteringNatural disasterGeotagged tweetNaive bayes classifier